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Python for Probability, Statistics, and Machine Learning (Hardcover, 3rd ed. 2022)
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Python for Probability, Statistics, and Machine Learning (Hardcover, 3rd ed. 2022)
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Using a novel integration of mathematics and Python codes, this
book illustrates the fundamental concepts that link probability,
statistics, and machine learning, so that the reader can not only
employ statistical and machine learning models using modern Python
modules, but also understand their relative strengths and
weaknesses. To clearly connect theoretical concepts to practical
implementations, the author provides many worked-out examples along
with "Programming Tips" that encourage the reader to write quality
Python code. The entire text, including all the figures and
numerical results, is reproducible using the Python codes provided,
thus enabling readers to follow along by experimenting with the
same code on their own computers. Modern Python modules like
Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray,
Tensorflow, and Keras are used to implement and visualize important
machine learning concepts like the bias/variance trade-off,
cross-validation, interpretability, and regularization. Many
abstract mathematical ideas, such as modes of convergence in
probability, are explained and illustrated with concrete numerical
examples. This book is suitable for anyone with undergraduate-level
experience with probability, statistics, or machine learning and
with rudimentary knowledge of Python programming.
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